library(png)
## First consolidate the available files into a single table
    
      path <- "~/Box Sync/Four model compare/Module 2"
           
     
           setwd(path)
    myfiles_full <- list.dirs()
    analyze_this_many <- length(myfiles_full)
    
    available_files <- matrix(NA, 1, 1)
    
        
    for(i in 1: analyze_this_many){
    available_files <- rbind(available_files , as.matrix(list.files(myfiles_full[i], full.names = TRUE)))
    }
    dim(available_files)
    
    split.file.name <- strsplit(available_files[10], split = "_") 
    
    
    
 
available <- list.files()
files <- matrix(rep(NA, 62), length(available), 62)
dim(files)
i <- 10


for(i in 1:length(available)){
load(available[i])
name <- unlist(strsplit(available[i], split="_"))
files[i,] <- c(as.vector(matrix(name, 1,35)),matrix(Sim_statistics[[1]], 1, 27))

}


colnames(files) <-  c(

    NA,
    "background_takeover_type" ,
    NA,
    "replicate",
    NA,
    "Model_type",
    rep(NA,2),
    "speciation_of_Env_NonD",
    "speciation_of_Env_D",
    "speciation_of_For",
    "speciation_of_Dom",
    NA,
    "extinction_of_Env_NonD",
    "extinction_of_Env_D",
    "extinction_of_For",
    "extinction_of_Dom",
    NA,
    "P.diffusion_Target_forager",
    "P.diffusion_Target_domesticator",
    "P.diffusion_Source_forager",
    "P.diffusion_Source_domesticator",
    NA,
    "P.takeover_Target_forager",
    "P.takeover_Target_domesticator",
    "P.takeover_Source_forager",
    "P.takeover_Source_domesticator",
    NA,
    "arisal_of_Env_NonD",
    "arisal_of_Env_D",
    "arisal_of_For",
    "arisal_of_Dom",
    
    NA, 
    "timesteps", 
    NA,
        
    "number_of_branches",
    "Pylo_diversity_is_sum_of_BL",
    "average_phylogenetic_diversity_is_mean_of_BL",
    "variance_Pylo_diversity_is_variance_of_BL",

    "F_quadratic_entropy_is_sum_of_PD",
    "Mean_pairwise_distance",
    "variance_pairwise_distance",

    "Evolutionary_distinctiveness_sum",
    "mean_Phylogenetic_isolation",
    "variance_Phylogenetic_isolation",

    "gamma",
    "gamma_p_value",
    "speciation_rate",
    "extinction_rate",
    "extinction_per_speciation",
    "speciation_minus_extinction",
    "trait_1_speciation",
    "trait_2_speciation" ,
    "trait_1_extinction" ,
    "trait_2_extinction" ,
    "transition_from_trait_1_to_2" ,
    "transition_from_trait_2_to_1" ,
    "transition_rate_ratio_1to2_over_2to1" ,
    "Phylogenetic_signal",
    "spatial.tests.fora",
    "spatial.tests.dom",
    "prevalence"
    
    
  )

results_table <- as.data.frame(files)
head(results_table)
dim(results_table)
Concatenated_data <- results_table
save(Concatenated_data, file="~/Desktop/Four_model_compare_results.Rdata")

one <- subset(results_table, Model_type=="01" )
two <- subset(results_table, Model_type=="02" )
three <- subset(results_table, Model_type=="03" )
four <- subset(results_table, Model_type=="04" )
crop <- min(length(one[,1]),
length(two[,1]),
length(three[,1]),
length(four[,1]))
one <- one[1:crop,]
two <- two[1:crop,]
three <- three[1:crop,]
four <- four[1:crop,]

Concatenated_data <- rbind(one, two, three, four)
dim(Concatenated_data)
save(Concatenated_data, file="~/Desktop/Four_model_compare_results.Rdata")
crop
## First consolidate the available files into a single table
    
      path <- "~/Box Sync/Four model compare/Module 2 extinct"
           
     
           setwd(path)
Error in setwd(path) : cannot change working directory
load('~/Box Sync/colliding ranges/Simulations_humans/Results/Four_model_compare_results_extinct.Rdata')
extinct <- Concatenated_data
load('~/Box Sync/colliding ranges/Simulations_humans/Results/Four_model_compare_results.Rdata')
extant <- Concatenated_data
names(extinct)
 [1] NA                                            
 [2] "background_takeover_type"                    
 [3] NA                                            
 [4] "replicate"                                   
 [5] NA                                            
 [6] "Model_type"                                  
 [7] NA                                            
 [8] NA                                            
 [9] "speciation_of_Env_NonD"                      
[10] "speciation_of_Env_D"                         
[11] "speciation_of_For"                           
[12] "speciation_of_Dom"                           
[13] NA                                            
[14] "extinction_of_Env_NonD"                      
[15] "extinction_of_Env_D"                         
[16] "extinction_of_For"                           
[17] "extinction_of_Dom"                           
[18] NA                                            
[19] "P.diffusion_Target_forager"                  
[20] "P.diffusion_Target_domesticator"             
[21] "P.diffusion_Source_forager"                  
[22] "P.diffusion_Source_domesticator"             
[23] NA                                            
[24] "P.takeover_Target_forager"                   
[25] "P.takeover_Target_domesticator"              
[26] "P.takeover_Source_forager"                   
[27] "P.takeover_Source_domesticator"              
[28] NA                                            
[29] "arisal_of_Env_NonD"                          
[30] "arisal_of_Env_D"                             
[31] "arisal_of_For"                               
[32] "arisal_of_Dom"                               
[33] NA                                            
[34] "timesteps"                                   
[35] NA                                            
[36] "number_of_branches"                          
[37] "Pylo_diversity_is_sum_of_BL"                 
[38] "average_phylogenetic_diversity_is_mean_of_BL"
[39] "variance_Pylo_diversity_is_variance_of_BL"   
[40] "F_quadratic_entropy_is_sum_of_PD"            
[41] "Mean_pairwise_distance"                      
[42] "variance_pairwise_distance"                  
[43] "Evolutionary_distinctiveness_sum"            
[44] "mean_Phylogenetic_isolation"                 
[45] "variance_Phylogenetic_isolation"             
[46] "gamma"                                       
[47] "gamma_p_value"                               
[48] "speciation_rate"                             
[49] "extinction_rate"                             
[50] "extinction_per_speciation"                   
[51] "speciation_minus_extinction"                 
[52] "trait_1_speciation"                          
[53] "trait_2_speciation"                          
[54] "trait_1_extinction"                          
[55] "trait_2_extinction"                          
[56] "transition_from_trait_1_to_2"                
[57] "transition_from_trait_2_to_1"                
[58] "transition_rate_ratio_1to2_over_2to1"        
[59] "Phylogenetic_signal"                         
[60] "spatial.tests.fora"                          
[61] "spatial.tests.dom"                           
[62] "prevalence"                                  
names(extant)
 [1] NA                                            
 [2] "background_takeover_type"                    
 [3] "NA.1"                                        
 [4] "replicate"                                   
 [5] "NA.2"                                        
 [6] "Model_type"                                  
 [7] "NA.3"                                        
 [8] "NA.4"                                        
 [9] "speciation_of_Env_NonD"                      
[10] "speciation_of_Env_D"                         
[11] "speciation_of_For"                           
[12] "speciation_of_Dom"                           
[13] "NA.5"                                        
[14] "extinction_of_Env_NonD"                      
[15] "extinction_of_Env_D"                         
[16] "extinction_of_For"                           
[17] "extinction_of_Dom"                           
[18] "NA.6"                                        
[19] "P.diffusion_Target_forager"                  
[20] "P.diffusion_Target_domesticator"             
[21] "P.diffusion_Source_forager"                  
[22] "P.diffusion_Source_domesticator"             
[23] "NA.7"                                        
[24] "P.takeover_Target_forager"                   
[25] "P.takeover_Target_domesticator"              
[26] "P.takeover_Source_forager"                   
[27] "P.takeover_Source_domesticator"              
[28] "NA.8"                                        
[29] "arisal_of_Env_NonD"                          
[30] "arisal_of_Env_D"                             
[31] "arisal_of_For"                               
[32] "arisal_of_Dom"                               
[33] "NA.9"                                        
[34] "timesteps"                                   
[35] "NA.10"                                       
[36] "number_of_branches"                          
[37] "Pylo_diversity_is_sum_of_BL"                 
[38] "average_phylogenetic_diversity_is_mean_of_BL"
[39] "variance_Pylo_diversity_is_variance_of_BL"   
[40] "F_quadratic_entropy_is_sum_of_PD"            
[41] "Mean_pairwise_distance"                      
[42] "variance_pairwise_distance"                  
[43] "Evolutionary_distinctiveness_sum"            
[44] "mean_Phylogenetic_isolation"                 
[45] "variance_Phylogenetic_isolation"             
[46] "gamma"                                       
[47] "gamma_p_value"                               
[48] "speciation_rate"                             
[49] "extinction_rate"                             
[50] "extinction_per_speciation"                   
[51] "speciation_minus_extinction"                 
[52] "trait_1_speciation"                          
[53] "trait_2_speciation"                          
[54] "trait_1_extinction"                          
[55] "trait_2_extinction"                          
[56] "transition_from_trait_1_to_2"                
[57] "transition_from_trait_2_to_1"                
[58] "transition_rate_ratio_1to2_over_2to1"        
[59] "Phylogenetic_signal"                         
[60] "spatial.tests.fora"                          
[61] "spatial.tests.dom"                           
[62] "prevalence"                                  
for(i in c(9,10,11,12,14,15,16,17,19,20,21,22,24,25,26,27,29,30,31,32)){
    extinct[which(is.nan(as.numeric(as.character(extinct[, i]))) == TRUE), i] <- NA
}
for(i in c(9,10,11,12,14,15,16,17,19,20,21,22,24,25,26,27,29,30,31,32)){
    extant[which(is.nan(as.numeric(as.character(extant[, i]))) == TRUE), i] <- NA
}
i <- 19
for(i in c(20,21,24,25,26,27)){
    extinct[which(as.numeric(as.character(extinct[, i])) == 0), i] <- NA
}
for(i in c(20,21,24,25,26,27)){
    extant[which(as.numeric(as.character(extant[, i])) == 0), i] <- NA
}
xlimit <- c(0,1)
ylimit <- c(0,600)
maincex <- 0.9
png(file="Global_success_rate_per_parameter.png", width=8.5, height=11, units="in", res=300)
par(mfrow=c(5,4), mar=c(3,3,3,0))
hist(as.numeric(as.character(extinct[,9])), main="speciation of F in F env", col=adjustcolor("firebrick", alpha=0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,9])), main="speciation of F in F env", col=adjustcolor("cornflowerblue", alpha=0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[,10])), main="speciation of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,10])), main="speciation of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[,11])), main="speciation of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,11])), main="speciation of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[,12])), main="speciation of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,12])), main="speciation of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
#######
hist(as.numeric(as.character(extinct[, 14])), main="extinction of F in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 14])), main="extinction of F in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 15])), main="extinction of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 15])), main="extinction of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 16])), main="extinction of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 16])), main="extinction of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 17])), main="extinction of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 17])), main="extinction of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
######
hist(as.numeric(as.character(extinct[, 29])), main="arisal of F in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 29])), main="arisal of F in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 30])), main="arisal of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 30])), main="arisal of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 31])), main="arisal of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 31])), main="arisal of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 32])), main="arisal of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 32])), main="arisal of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
######
hist(as.numeric(as.character(extinct[, 19])), main="NOPE -- Diffusion: source F, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex)
hist(as.numeric(as.character(extant[, 19])), main="NOPE -- Diffusion: source F, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 20])), main="Diffusion: source D, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 20])), main="Diffusion: source D, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 21])), main="Diffusion: source F, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 21])), main="Diffusion: source F, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 22])), main="NOPE -- Diffusion: source D, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex)
hist(as.numeric(as.character(extant[, 22])), main="NOPE -- Diffusion: source D, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex, add=TRUE)
####
hist(as.numeric(as.character(extinct[, 24])), main="Takeover: source F, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 24])), main="Takeover: source F, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 25])), main="Takeover: source D, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 25])), main="Takeover: source D, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 26])), main="Takeover: source F, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 26])), main="Takeover: source F, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 27])), main="Takeover: source D, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 27])), main="Takeover: source D, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
dev.off()
null device 
          1 
png(file="extiction minus extant per outcome.png", width=8.5, height=11, units="in", res=300)
par(mfrow=c(3,1))
plot(as.numeric(as.character(extinct[,9])), as.numeric(as.character(extinct[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("firebrick", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
plot(as.numeric(as.character(extant[,9])), as.numeric(as.character(extant[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("cornflowerblue", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
plot(as.numeric(as.character(extinct[,9])), as.numeric(as.character(extinct[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("firebrick", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
points(as.numeric(as.character(extant[,9])), as.numeric(as.character(extant[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("cornflowerblue", alpha=0.2), pch=19, cex=0.6)
dev.off()
null device 
          1 
load('~/Box Sync/colliding ranges/Simulations_humans/Available trees/real.analysis.RData')
#Concatenated_data <- Concatenated_data[Concatenated_data[, 2] == "stats.no.bTO", ]
#Concatenated_data <- Concatenated_data[Concatenated_data[, 6] != "05", ]
# Concatenated_data[, 6] <- as.numeric(Concatenated_data[, 6])
# # Concatenated_data[original[, 2] == "background_takeover", 6] <-  Concatenated_data[original[, 2] == "background_takeover", 6] + 4
Concatenated_data[, 6] <- factor(Concatenated_data[, 6])
head(Concatenated_data)
names(Concatenated_data)
 [1] NA                                            
 [2] "background_takeover_type"                    
 [3] "NA.1"                                        
 [4] "replicate"                                   
 [5] "NA.2"                                        
 [6] "Model_type"                                  
 [7] "NA.3"                                        
 [8] "NA.4"                                        
 [9] "speciation_of_Env_NonD"                      
[10] "speciation_of_Env_D"                         
[11] "speciation_of_For"                           
[12] "speciation_of_Dom"                           
[13] "NA.5"                                        
[14] "extinction_of_Env_NonD"                      
[15] "extinction_of_Env_D"                         
[16] "extinction_of_For"                           
[17] "extinction_of_Dom"                           
[18] "NA.6"                                        
[19] "P.diffusion_Target_forager"                  
[20] "P.diffusion_Target_domesticator"             
[21] "P.diffusion_Source_forager"                  
[22] "P.diffusion_Source_domesticator"             
[23] "NA.7"                                        
[24] "P.takeover_Target_forager"                   
[25] "P.takeover_Target_domesticator"              
[26] "P.takeover_Source_forager"                   
[27] "P.takeover_Source_domesticator"              
[28] "NA.8"                                        
[29] "arisal_of_Env_NonD"                          
[30] "arisal_of_Env_D"                             
[31] "arisal_of_For"                               
[32] "arisal_of_Dom"                               
[33] "NA.9"                                        
[34] "timesteps"                                   
[35] "NA.10"                                       
[36] "number_of_branches"                          
[37] "Pylo_diversity_is_sum_of_BL"                 
[38] "average_phylogenetic_diversity_is_mean_of_BL"
[39] "variance_Pylo_diversity_is_variance_of_BL"   
[40] "F_quadratic_entropy_is_sum_of_PD"            
[41] "Mean_pairwise_distance"                      
[42] "variance_pairwise_distance"                  
[43] "Evolutionary_distinctiveness_sum"            
[44] "mean_Phylogenetic_isolation"                 
[45] "variance_Phylogenetic_isolation"             
[46] "gamma"                                       
[47] "gamma_p_value"                               
[48] "speciation_rate"                             
[49] "extinction_rate"                             
[50] "extinction_per_speciation"                   
[51] "speciation_minus_extinction"                 
[52] "trait_1_speciation"                          
[53] "trait_2_speciation"                          
[54] "trait_1_extinction"                          
[55] "trait_2_extinction"                          
[56] "transition_from_trait_1_to_2"                
[57] "transition_from_trait_2_to_1"                
[58] "transition_rate_ratio_1to2_over_2to1"        
[59] "Phylogenetic_signal"                         
[60] "spatial.tests.fora"                          
[61] "spatial.tests.dom"                           
[62] "prevalence"                                  
PCAdata <- Concatenated_data[, -(1:35)]
PCAdata <- PCAdata[, -12]
PCAdata <- apply(PCAdata, 2, as.numeric)
remove <- apply(is.na(PCAdata), 1, any)
PCAdata <- PCAdata[!remove, ]
# Predictions
library(randomForest)
data.analysis.comp2 <- data.frame("Model" = as.factor(Concatenated_data[!remove, 6]),
                                  PCAdata)
data.analysis.comp2$sprate <- data.analysis.comp2$trait_1_speciation/data.analysis.comp2$trait_2_speciation
data.analysis.comp2$extrate <- data.analysis.comp2$trait_1_extinction/data.analysis.comp2$trait_2_extinction
#load("Real_phy/real.analysis.RData")
a <- as.data.frame(real.analysis$results_summary_of_single_value_outputs)
a$sprate <- a$trait_1_speciation / a$trait_2_speciation
a$extrate <- a$trait_1_extinction / a$trait_2_extinction
data.analysis.comp3 <- data.analysis.comp2[, -c(2, 13:14, 16:20)]
#data.analysis.comp3 <- data.analysis.comp3[data.analysis.comp3$Model %in% 1:4, ]
#data.analysis.comp3$Model <- factor(data.analysis.comp3$Model)
#sub <- unlist(lapply(as.list(c(1:4)), function(x, y) {
#  sample(which(y$Model == x), min(table(data.analysis.comp3$Model)))},
#  y = data.analysis.comp3))
# data.analysis.comp3 <- data.analysis.comp3[sub, ]
fun <- function(x, y, per = .33) {sample(which(y$Model == x), round(table(y$Model)[1]*per))}
sub.test <- unlist(lapply(as.list(paste0(0, c(1:4))), fun,
                          y = data.analysis.comp3))
test2 <- data.analysis.comp3[sub.test, 2:ncol(data.analysis.comp3)]
test1 <- data.analysis.comp3[sub.test, 1]
train <- data.analysis.comp3[-sub.test, ]
(fit <- randomForest(Model ~ ., data=train, xtest = test2, ytest = test1, 
                    importance=TRUE, ntree=2400, keep.forest = TRUE))

Call:
 randomForest(formula = Model ~ ., data = train, xtest = test2,      ytest = test1, importance = TRUE, ntree = 2400, keep.forest = TRUE) 
               Type of random forest: classification
                     Number of trees: 2400
No. of variables tried at each split: 4

        OOB estimate of  error rate: 42.23%
Confusion matrix:
    01  02  03  04 class.error
01 956 150 377  77   0.3871795
02 204 704 149 457   0.5350066
03 308  52 924 229   0.3892928
04  67 187 277 883   0.3755304
                Test set error rate: 42.42%
Confusion matrix:
    01  02  03  04 class.error
01 461  82 187  38   0.3997396
02 108 361  84 215   0.5299479
03 142  23 485 118   0.3684896
04  33 127 146 462   0.3984375
predictions <- predict(fit, 
                       a,
                       type="prob")
plot(fit, ylim=c(0,1))

labs <- c("Basic", "+Diffusion", "+Takeover", "+Diffusion +Takeover")
# bar plot
png("Prob_aus.png", width = 25, height = 25, res = 300, units = "in")
par(mar = c(8, 8, 1, 1))
pred <- setNames(as.numeric(predictions), labs)
cols <- rev(c("darkgreen", "red", "blue", "darkorange1"))
barplot(pred, col = cols, ylab = "Proability", cex.lab = 3, cex.names = 2)
dev.off()
null device 
          1 
prop
          01        02        03        04
01 62.280130 13.723696 21.829763  4.678007
02 13.289902 64.409881  8.627678 27.764277
03 20.065147  4.757548 53.503185 13.912515
04  4.364821 17.108875 16.039375 53.645200
importance(fit)
                                                   01        02        03
Pylo_diversity_is_sum_of_BL                  36.27731 26.313769 38.900228
average_phylogenetic_diversity_is_mean_of_BL 42.47717 32.482005 38.519904
variance_Pylo_diversity_is_variance_of_BL    42.15824 16.661635  9.266383
F_quadratic_entropy_is_sum_of_PD             24.95204 19.851036 36.048651
Mean_pairwise_distance                       31.03797 25.728248 33.157743
variance_pairwise_distance                   31.43706 29.867035 34.057818
Evolutionary_distinctiveness_sum             36.73711 26.605041 37.480353
mean_Phylogenetic_isolation                  40.52562 33.527197 41.801064
variance_Phylogenetic_isolation              24.57867  8.325148 37.260442
gamma                                        26.36037 21.591877 63.126688
extinction_per_speciation                     7.99549 41.950330 54.475002
transition_from_trait_1_to_2                 22.53100 22.856490 41.900317
transition_from_trait_2_to_1                 33.37167 33.715083 45.090491
transition_rate_ratio_1to2_over_2to1         50.20133 36.600927 35.430284
Phylogenetic_signal                          64.80645 43.118360 53.232738
spatial.tests.fora                           43.20987 86.600931 89.746230
spatial.tests.dom                            82.03166 30.318014 47.558336
prevalence                                   54.14112  9.616747 35.927323
sprate                                       41.34870 28.286586 85.062626
extrate                                      28.88581 24.078228 21.624649
                                                   04 MeanDecreaseAccuracy
Pylo_diversity_is_sum_of_BL                  45.76293             62.84990
average_phylogenetic_diversity_is_mean_of_BL 32.73694             67.29494
variance_Pylo_diversity_is_variance_of_BL    27.05678             54.35726
F_quadratic_entropy_is_sum_of_PD             43.18824             68.31908
Mean_pairwise_distance                       42.30806             63.22960
variance_pairwise_distance                   22.73712             60.82865
Evolutionary_distinctiveness_sum             44.98800             63.01409
mean_Phylogenetic_isolation                  33.38172             68.79250
variance_Phylogenetic_isolation              22.99055             49.94780
gamma                                        51.68400             89.28718
extinction_per_speciation                    54.09510             75.50858
transition_from_trait_1_to_2                 41.11630             58.91197
transition_from_trait_2_to_1                 40.76374             67.48847
transition_rate_ratio_1to2_over_2to1         42.40223             85.56456
Phylogenetic_signal                          40.17895             85.09066
spatial.tests.fora                           76.44656            125.15481
spatial.tests.dom                            53.97271            104.59956
prevalence                                   58.91447             79.90220
sprate                                       36.41170             92.93119
extrate                                      36.77120             58.20976
                                             MeanDecreaseGini
Pylo_diversity_is_sum_of_BL                          210.2330
average_phylogenetic_diversity_is_mean_of_BL         182.1725
variance_Pylo_diversity_is_variance_of_BL            164.4793
F_quadratic_entropy_is_sum_of_PD                     185.9903
Mean_pairwise_distance                               206.3388
variance_pairwise_distance                           183.6735
Evolutionary_distinctiveness_sum                     207.9225
mean_Phylogenetic_isolation                          184.8331
variance_Phylogenetic_isolation                      167.5043
gamma                                                213.3865
extinction_per_speciation                            251.9260
transition_from_trait_1_to_2                         255.1930
transition_from_trait_2_to_1                         262.6217
transition_rate_ratio_1to2_over_2to1                 211.2503
Phylogenetic_signal                                  303.1266
spatial.tests.fora                                   395.4726
spatial.tests.dom                                    250.7786
prevalence                                           226.0943
sprate                                               244.7882
extrate                                              190.3293
# Variables importance
imp <- importance(fit)
imp <- apply(imp, 2, function(x) (x - min(x))/(max(x) - min(x)))
imp <- imp[sort(imp[, 5], index.return = TRUE, decreasing = TRUE)$ix, ]
names <- rownames(imp)
names[names == "spatial.tests.fora"] <- "Space F"
names[names == "spatial.tests.dom"] <- "Space D"
names[names == "sprate"] <- "Sp(ratio)"
names[names == "transition_from_trait_1_to_2"] <- "TR(1-2)"
names[names == "transition_from_trait_2_to_1"] <- "TR(2-1)"
names[names == "Phylogenetic_signal"] <- "PhySig(D)"
names[names == "Evolutionary_distinctiveness_sum"] <- "EDsum"
names[names == "Pylo_diversity_is_sum_of_BL"] <- "PDsum"
names[names == "transition_rate_ratio_1to2_over_2to1"] <- "TR(ratio)"
names[names == "gamma"] <- "Gamma"
names[names == "mean_Phylogenetic_isolation"] <- "MPI"
names[names == "extrate"] <- "Ext(ratio)"
names[names == "average_phylogenetic_diversity_is_mean_of_BL"] <- "PDmean"
names[names == "extinction_per_speciation"] <- "DR"
names[names == "variance_Phylogenetic_isolation"] <- "VPI"
names[names == "F_quadratic_entropy_is_sum_of_PD"] <- "F"
names[names == "Mean_pairwise_distance"] <- "MPD"
names[names == "variance_Pylo_diversity_is_variance_of_BL"] <- "PDvar"
names[names == "variance_pairwise_distance"] <- "VPD"
png("var_import_all.png", width = 25, height = 25, unit="in", res=300)
par(mar = c(10, 18, 1, 1))
plot(x = rev(imp[, 5]), y = 1:nrow(imp), type = "l", yaxt = "n", 
     ylab = "", xlab = "Variable Importance",
     xlim = c(0, 1), lwd = 2, cex.lab = 4)
for (i in 1:nrow(imp)) {
  abline(h = i, lty = 3, col = "gray80")
}
abline(v = seq(0, 1, 1/19), lty = 3, col = "gray80")
lines(x = rev(imp[, 4]), y = 1:nrow(imp), col = "darkgreen", lwd = 2)
lines(x = rev(imp[, 3]), y = 1:nrow(imp), col = "red", lwd = 2)
lines(x = rev(imp[, 2]), y = 1:nrow(imp), col = "blue", lwd = 2)
lines(x = rev(imp[, 1]), y = 1:nrow(imp), col = "darkorange1", lwd = 2)
lines(x = rev(imp[, 5]), y = 1:nrow(imp), lwd = 3)
points(x = rev(imp[, 4]), y = 1:nrow(imp), col = "darkgreen", cex = 2)
points(x = rev(imp[, 3]), y = 1:nrow(imp), col = "red", cex = 2)
points(x = rev(imp[, 2]), y = 1:nrow(imp), col = "blue", cex = 2)
points(x = rev(imp[, 1]), y = 1:nrow(imp), col = "darkorange1", cex = 2)
points(x = rev(imp[, 5]), y = 1:nrow(imp), pch = 20, cex = 3)
text(y = 1:nrow(imp), x = par("usr")[1] - .17, labels = rev(names),
     srt = 0, pos = 4, xpd = T, cex = 4)
dev.off()
null device 
          1 
par(mfrow=c(2,3))
# Box plots
boxplot(spatial.tests.fora ~ Model, data = data.analysis.comp3)
abline(h = a$spatial.tests.fora, col = "red", lty = 2)
boxplot(spatial.tests.dom ~ Model, data = data.analysis.comp3)
abline(h = a$spatial.tests.fora, col = "red", lty = 2)
boxplot(log(sprate) ~ Model, data = data.analysis.comp3, ylim = c(-10, 10))
abline(h = log(a$sprate), col = "red", lty = 2)
boxplot(log(extrate) ~ Model, data = data.analysis.comp3, ylim = c(-10, 10))
abline(h = log(a$extrate), col = "red", lty = 2)
boxplot(log(transition_rate_ratio_1to2_over_2to1) ~ Model, data = data.analysis.comp3)
abline(h = log(a$sprate), col = "red", lty = 2)
boxplot(Phylogenetic_signal ~ Model, data = data.analysis.comp3, ylim = c(0, 1))
abline(h = a$Phylogenetic_signal, col = "red", lty = 2)

---
title: "D-place FARM documentation: Module 3"
author: "Ty Tuff, Bruno Vilela, and Carlos Botero"
date: 'project began: 15 May 2016, document updated: `r strftime(Sys.time(), format
  = "%d %B %Y")`'
output:
  html_notebook: default
  html_document: default
  pdf_document: default
  word_document: default
bibliography: FARM package.bib
---
```{r}
library(png)
```



```{r}
## First consolidate the available files into a single table
    
      path <- "~/Box Sync/Four model compare/Module 2"
           
     
           setwd(path)
    myfiles_full <- list.dirs()
    analyze_this_many <- length(myfiles_full)
    
    available_files <- matrix(NA, 1, 1)
    
        
    for(i in 1: analyze_this_many){
    available_files <- rbind(available_files , as.matrix(list.files(myfiles_full[i], full.names = TRUE)))
    }
    dim(available_files)
    
    split.file.name <- strsplit(available_files[10], split = "_") 
    
    
    
 
available <- list.files()
files <- matrix(rep(NA, 62), length(available), 62)
dim(files)
i <- 10


for(i in 1:length(available)){
load(available[i])
name <- unlist(strsplit(available[i], split="_"))
files[i,] <- c(as.vector(matrix(name, 1,35)),matrix(Sim_statistics[[1]], 1, 27))

}


colnames(files) <-  c(

	NA,
	"background_takeover_type" ,
	NA,
	"replicate",
	NA,
	"Model_type",
	rep(NA,2),
	"speciation_of_Env_NonD",
	"speciation_of_Env_D",
	"speciation_of_For",
	"speciation_of_Dom",
	NA,
	"extinction_of_Env_NonD",
	"extinction_of_Env_D",
	"extinction_of_For",
	"extinction_of_Dom",
	NA,
	"P.diffusion_Target_forager",
	"P.diffusion_Target_domesticator",
	"P.diffusion_Source_forager",
	"P.diffusion_Source_domesticator",
	NA,
	"P.takeover_Target_forager",
	"P.takeover_Target_domesticator",
	"P.takeover_Source_forager",
	"P.takeover_Source_domesticator",
	NA,
	"arisal_of_Env_NonD",
	"arisal_of_Env_D",
	"arisal_of_For",
	"arisal_of_Dom",
	
	NA, 
	"timesteps", 
	NA,
        
    "number_of_branches",
	"Pylo_diversity_is_sum_of_BL",
	"average_phylogenetic_diversity_is_mean_of_BL",
	"variance_Pylo_diversity_is_variance_of_BL",

	"F_quadratic_entropy_is_sum_of_PD",
	"Mean_pairwise_distance",
	"variance_pairwise_distance",

	"Evolutionary_distinctiveness_sum",
	"mean_Phylogenetic_isolation",
	"variance_Phylogenetic_isolation",

	"gamma",
	"gamma_p_value",
	"speciation_rate",
	"extinction_rate",
	"extinction_per_speciation",
	"speciation_minus_extinction",
	"trait_1_speciation",
  	"trait_2_speciation" ,
  	"trait_1_extinction" ,
  	"trait_2_extinction" ,
  	"transition_from_trait_1_to_2" ,
  	"transition_from_trait_2_to_1" ,
  	"transition_rate_ratio_1to2_over_2to1" ,
  	"Phylogenetic_signal",
  	"spatial.tests.fora",
  	"spatial.tests.dom",
  	"prevalence"
  	
    
  )

results_table <- as.data.frame(files)
head(results_table)
dim(results_table)
Concatenated_data <- results_table
save(Concatenated_data, file="~/Desktop/Four_model_compare_results.Rdata")

one <- subset(results_table, Model_type=="01" )
two <- subset(results_table, Model_type=="02" )
three <- subset(results_table, Model_type=="03" )
four <- subset(results_table, Model_type=="04" )
crop <- min(length(one[,1]),
length(two[,1]),
length(three[,1]),
length(four[,1]))
one <- one[1:crop,]
two <- two[1:crop,]
three <- three[1:crop,]
four <- four[1:crop,]

Concatenated_data <- rbind(one, two, three, four)
dim(Concatenated_data)
save(Concatenated_data, file="~/Desktop/Four_model_compare_results.Rdata")
crop

```




```{r}
## First consolidate the available files into a single table
    
      path <- "~/Box Sync/Four model compare/Module 2 extinct"
           
     
           setwd(path)
    myfiles_full <- list.dirs()
    analyze_this_many <- length(myfiles_full)
    
    available_files <- matrix(NA, 1, 1)
    
        
    for(i in 1: analyze_this_many){
    available_files <- rbind(available_files , as.matrix(list.files(myfiles_full[i], full.names = TRUE)))
    }
    dim(available_files)
    
    split.file.name <- strsplit(available_files[10], split = "_") 
    
    
    
 
available <- list.files()
files <- matrix(rep(NA, 62), length(available), 62)
dim(files)
i <- 10


for(i in 1:length(available)){
load(available[i])
name <- unlist(strsplit(available[i], split="_"))
files[i,] <- c(as.vector(matrix(name, 1,35)),matrix(Sim_statistics[[1]], 1, 27))

}


colnames(files) <-  c(

	NA,
	"background_takeover_type" ,
	NA,
	"replicate",
	NA,
	"Model_type",
	rep(NA,2),
	"speciation_of_Env_NonD",
	"speciation_of_Env_D",
	"speciation_of_For",
	"speciation_of_Dom",
	NA,
	"extinction_of_Env_NonD",
	"extinction_of_Env_D",
	"extinction_of_For",
	"extinction_of_Dom",
	NA,
	"P.diffusion_Target_forager",
	"P.diffusion_Target_domesticator",
	"P.diffusion_Source_forager",
	"P.diffusion_Source_domesticator",
	NA,
	"P.takeover_Target_forager",
	"P.takeover_Target_domesticator",
	"P.takeover_Source_forager",
	"P.takeover_Source_domesticator",
	NA,
	"arisal_of_Env_NonD",
	"arisal_of_Env_D",
	"arisal_of_For",
	"arisal_of_Dom",
	
	NA, 
	"timesteps", 
	NA,
        
    "number_of_branches",
	"Pylo_diversity_is_sum_of_BL",
	"average_phylogenetic_diversity_is_mean_of_BL",
	"variance_Pylo_diversity_is_variance_of_BL",

	"F_quadratic_entropy_is_sum_of_PD",
	"Mean_pairwise_distance",
	"variance_pairwise_distance",

	"Evolutionary_distinctiveness_sum",
	"mean_Phylogenetic_isolation",
	"variance_Phylogenetic_isolation",

	"gamma",
	"gamma_p_value",
	"speciation_rate",
	"extinction_rate",
	"extinction_per_speciation",
	"speciation_minus_extinction",
	"trait_1_speciation",
  	"trait_2_speciation" ,
  	"trait_1_extinction" ,
  	"trait_2_extinction" ,
  	"transition_from_trait_1_to_2" ,
  	"transition_from_trait_2_to_1" ,
  	"transition_rate_ratio_1to2_over_2to1" ,
  	"Phylogenetic_signal",
  	"spatial.tests.fora",
  	"spatial.tests.dom",
  	"prevalence"
  	
    
  )

Concatenated_data <- as.data.frame(files)
head(Concatenated_data)
dim(Concatenated_data)

save(Concatenated_data, file="~/Desktop/Four_model_compare_results_extinct.Rdata")


```


```{r}
load('~/Box Sync/colliding ranges/Simulations_humans/Results/Four_model_compare_results_extinct.Rdata')
extinct <- Concatenated_data
load('~/Box Sync/colliding ranges/Simulations_humans/Results/Four_model_compare_results.Rdata')
extant <- Concatenated_data
names(extinct)
names(extant)
```



```{r}

for(i in c(9,10,11,12,14,15,16,17,19,20,21,22,24,25,26,27,29,30,31,32)){
	extinct[which(is.nan(as.numeric(as.character(extinct[, i]))) == TRUE), i] <- NA
}

for(i in c(9,10,11,12,14,15,16,17,19,20,21,22,24,25,26,27,29,30,31,32)){
	extant[which(is.nan(as.numeric(as.character(extant[, i]))) == TRUE), i] <- NA
}

i <- 19
for(i in c(20,21,24,25,26,27)){
	extinct[which(as.numeric(as.character(extinct[, i])) == 0), i] <- NA
}

for(i in c(20,21,24,25,26,27)){
	extant[which(as.numeric(as.character(extant[, i])) == 0), i] <- NA
}


xlimit <- c(0,1)
ylimit <- c(0,600)
maincex <- 0.9

png(file="Global_success_rate_per_parameter.png", width=8.5, height=11, units="in", res=300)

par(mfrow=c(5,4), mar=c(3,3,3,0))


hist(as.numeric(as.character(extinct[,9])), main="speciation of F in F env", col=adjustcolor("firebrick", alpha=0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,9])), main="speciation of F in F env", col=adjustcolor("cornflowerblue", alpha=0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)


hist(as.numeric(as.character(extinct[,10])), main="speciation of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,10])), main="speciation of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)


hist(as.numeric(as.character(extinct[,11])), main="speciation of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,11])), main="speciation of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[,12])), main="speciation of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,12])), main="speciation of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

#######

hist(as.numeric(as.character(extinct[, 14])), main="extinction of F in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 14])), main="extinction of F in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)



hist(as.numeric(as.character(extinct[, 15])), main="extinction of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 15])), main="extinction of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 16])), main="extinction of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 16])), main="extinction of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 17])), main="extinction of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 17])), main="extinction of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

######

hist(as.numeric(as.character(extinct[, 29])), main="arisal of F in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 29])), main="arisal of F in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)



hist(as.numeric(as.character(extinct[, 30])), main="arisal of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 30])), main="arisal of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 31])), main="arisal of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 31])), main="arisal of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 32])), main="arisal of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 32])), main="arisal of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

######

hist(as.numeric(as.character(extinct[, 19])), main="NOPE -- Diffusion: source F, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex)
hist(as.numeric(as.character(extant[, 19])), main="NOPE -- Diffusion: source F, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex, add=TRUE)



hist(as.numeric(as.character(extinct[, 20])), main="Diffusion: source D, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 20])), main="Diffusion: source D, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 21])), main="Diffusion: source F, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 21])), main="Diffusion: source F, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 22])), main="NOPE -- Diffusion: source D, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex)
hist(as.numeric(as.character(extant[, 22])), main="NOPE -- Diffusion: source D, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex, add=TRUE)

####

hist(as.numeric(as.character(extinct[, 24])), main="Takeover: source F, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 24])), main="Takeover: source F, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)



hist(as.numeric(as.character(extinct[, 25])), main="Takeover: source D, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 25])), main="Takeover: source D, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 26])), main="Takeover: source F, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 26])), main="Takeover: source F, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 27])), main="Takeover: source D, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 27])), main="Takeover: source D, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)


dev.off()






```


![](Global_success_rate_per_parameter.png)


```{r}



png(file="extiction minus extant per outcome.png", width=8.5, height=11, units="in", res=300)
par(mfrow=c(3,1))

plot(as.numeric(as.character(extinct[,9])), as.numeric(as.character(extinct[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("firebrick", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
plot(as.numeric(as.character(extant[,9])), as.numeric(as.character(extant[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("cornflowerblue", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))

plot(as.numeric(as.character(extinct[,9])), as.numeric(as.character(extinct[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("firebrick", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
points(as.numeric(as.character(extant[,9])), as.numeric(as.character(extant[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("cornflowerblue", alpha=0.2), pch=19, cex=0.6)


dev.off()


```

![](extiction minus extant per outcome.png)



```{r}


load('~/Box Sync/colliding ranges/Simulations_humans/Available trees/real.analysis.RData')



#Concatenated_data <- Concatenated_data[Concatenated_data[, 2] == "stats.no.bTO", ]
#Concatenated_data <- Concatenated_data[Concatenated_data[, 6] != "05", ]
# Concatenated_data[, 6] <- as.numeric(Concatenated_data[, 6])
# # Concatenated_data[original[, 2] == "background_takeover", 6] <-  Concatenated_data[original[, 2] == "background_takeover", 6] + 4
Concatenated_data[, 6] <- factor(Concatenated_data[, 6])
#head(Concatenated_data)
#names(Concatenated_data)

PCAdata <- Concatenated_data[, -(1:35)]
PCAdata <- PCAdata[, -12]
PCAdata <- apply(PCAdata, 2, as.numeric)
remove <- apply(is.na(PCAdata), 1, any)
PCAdata <- PCAdata[!remove, ]

# Predictions
library(randomForest)

data.analysis.comp2 <- data.frame("Model" = as.factor(Concatenated_data[!remove, 6]),
                                  PCAdata)
data.analysis.comp2$sprate <- data.analysis.comp2$trait_1_speciation/data.analysis.comp2$trait_2_speciation
data.analysis.comp2$extrate <- data.analysis.comp2$trait_1_extinction/data.analysis.comp2$trait_2_extinction


#load("Real_phy/real.analysis.RData")
a <- as.data.frame(real.analysis$results_summary_of_single_value_outputs)
a$sprate <- a$trait_1_speciation / a$trait_2_speciation
a$extrate <- a$trait_1_extinction / a$trait_2_extinction

data.analysis.comp3 <- data.analysis.comp2[, -c(2, 13:14, 16:20)]
#data.analysis.comp3 <- data.analysis.comp3[data.analysis.comp3$Model %in% 1:4, ]
#data.analysis.comp3$Model <- factor(data.analysis.comp3$Model)
#sub <- unlist(lapply(as.list(c(1:4)), function(x, y) {
#  sample(which(y$Model == x), min(table(data.analysis.comp3$Model)))},
#  y = data.analysis.comp3))
# data.analysis.comp3 <- data.analysis.comp3[sub, ]
fun <- function(x, y, per = .33) {sample(which(y$Model == x), round(table(y$Model)[1]*per))}

sub.test <- unlist(lapply(as.list(paste0(0, c(1:4))), fun,
                          y = data.analysis.comp3))
test2 <- data.analysis.comp3[sub.test, 2:ncol(data.analysis.comp3)]
test1 <- data.analysis.comp3[sub.test, 1]
train <- data.analysis.comp3[-sub.test, ]
(fit <- randomForest(Model ~ ., data=train, xtest = test2, ytest = test1, 
                    importance=TRUE, ntree=2400, keep.forest = TRUE))

predictions <- predict(fit, 
                       a,
                       type="prob")


```





```{r}

plot(fit, ylim=c(0,1))

```




```{r}
labs <- c("Basic", "+Diffusion", "+Takeover", "+Diffusion +Takeover")

```


```{r}
# bar plot
png("Prob_aus.png", width = 25, height = 25, res = 300, units = "in")
par(mar = c(8, 8, 1, 1))
pred <- setNames(as.numeric(predictions), labs)
cols <- rev(c("darkgreen", "red", "blue", "darkorange1"))
barplot(pred, col = cols, ylab = "Proability", cex.lab = 3, cex.names = 2)
dev.off()
```

![](Prob_aus.png)


```{r}
# Plot confusion matrix
png("Conffusion_matrix_all.png", width = 25, height = 25, res=300, units="in")
par(mar = c(10, 11, 1, 1))
colors1 <- colorRampPalette(colors = c("#f0f0f0", "#bdbdbd","#636363"))
prop <- apply(fit$confusion[, -5], 2, function(x){x / sum(x)}) * 100

image(prop, col = colors1(20), axes=FALSE)
axis(1, at=c(0, .33, .66, 1), labels=labs, tick = FALSE, line = FALSE, cex.axis = 3.5, pos = -.19)
axis(2, at=c(0, .33, .66, 1), labels=labs, tick = FALSE, line = FALSE, cex.axis = 3.5)
mtext("ACTUAL", side = 1, padj = 3, cex = 4)
mtext("PREDICTED", side = 2, padj = -3, cex = 4)

for(i in 1:4) {
  for(j in 1:4) {
    text(x = c(0, .33, .66, 1)[i], y = c(0, .33, .66, 1)[j], paste0(round(prop[i, j], 2), "%"),
         cex = 5)
  }
}
dev.off()
```


![](Conffusion_matrix_all.png)


```{r}
importance(fit)
```


```{r}
# Variables importance

imp <- importance(fit)
imp <- apply(imp, 2, function(x) (x - min(x))/(max(x) - min(x)))
imp <- imp[sort(imp[, 5], index.return = TRUE, decreasing = TRUE)$ix, ]


names <- rownames(imp)
names[names == "spatial.tests.fora"] <- "Space F"
names[names == "spatial.tests.dom"] <- "Space D"
names[names == "sprate"] <- "Sp(ratio)"
names[names == "transition_from_trait_1_to_2"] <- "TR(1-2)"
names[names == "transition_from_trait_2_to_1"] <- "TR(2-1)"
names[names == "Phylogenetic_signal"] <- "PhySig(D)"
names[names == "Evolutionary_distinctiveness_sum"] <- "EDsum"
names[names == "Pylo_diversity_is_sum_of_BL"] <- "PDsum"
names[names == "transition_rate_ratio_1to2_over_2to1"] <- "TR(ratio)"
names[names == "gamma"] <- "Gamma"
names[names == "mean_Phylogenetic_isolation"] <- "MPI"
names[names == "extrate"] <- "Ext(ratio)"
names[names == "average_phylogenetic_diversity_is_mean_of_BL"] <- "PDmean"
names[names == "extinction_per_speciation"] <- "DR"
names[names == "variance_Phylogenetic_isolation"] <- "VPI"
names[names == "F_quadratic_entropy_is_sum_of_PD"] <- "F"
names[names == "Mean_pairwise_distance"] <- "MPD"
names[names == "variance_Pylo_diversity_is_variance_of_BL"] <- "PDvar"
names[names == "variance_pairwise_distance"] <- "VPD"


png("var_import_all.png", width = 25, height = 25, unit="in", res=300)
par(mar = c(10, 18, 1, 1))
plot(x = rev(imp[, 5]), y = 1:nrow(imp), type = "l", yaxt = "n", 
     ylab = "", xlab = "Variable Importance",
     xlim = c(0, 1), lwd = 2, cex.lab = 4)
for (i in 1:nrow(imp)) {
  abline(h = i, lty = 3, col = "gray80")
}
abline(v = seq(0, 1, 1/19), lty = 3, col = "gray80")

lines(x = rev(imp[, 4]), y = 1:nrow(imp), col = "darkgreen", lwd = 2)
lines(x = rev(imp[, 3]), y = 1:nrow(imp), col = "red", lwd = 2)
lines(x = rev(imp[, 2]), y = 1:nrow(imp), col = "blue", lwd = 2)
lines(x = rev(imp[, 1]), y = 1:nrow(imp), col = "darkorange1", lwd = 2)
lines(x = rev(imp[, 5]), y = 1:nrow(imp), lwd = 3)

points(x = rev(imp[, 4]), y = 1:nrow(imp), col = "darkgreen", cex = 2)
points(x = rev(imp[, 3]), y = 1:nrow(imp), col = "red", cex = 2)
points(x = rev(imp[, 2]), y = 1:nrow(imp), col = "blue", cex = 2)
points(x = rev(imp[, 1]), y = 1:nrow(imp), col = "darkorange1", cex = 2)
points(x = rev(imp[, 5]), y = 1:nrow(imp), pch = 20, cex = 3)


text(y = 1:nrow(imp), x = par("usr")[1] - .17, labels = rev(names),
     srt = 0, pos = 4, xpd = T, cex = 4)
dev.off()
```

![](var_import_all.png)




```{r}
par(mfrow=c(2,3))

# Box plots
boxplot(spatial.tests.fora ~ Model, data = data.analysis.comp3)
abline(h = a$spatial.tests.fora, col = "red", lty = 2)

boxplot(spatial.tests.dom ~ Model, data = data.analysis.comp3)
abline(h = a$spatial.tests.fora, col = "red", lty = 2)

boxplot(log(sprate) ~ Model, data = data.analysis.comp3, ylim = c(-10, 10))
abline(h = log(a$sprate), col = "red", lty = 2)

boxplot(log(extrate) ~ Model, data = data.analysis.comp3, ylim = c(-10, 10))
abline(h = log(a$extrate), col = "red", lty = 2)

boxplot(log(transition_rate_ratio_1to2_over_2to1) ~ Model, data = data.analysis.comp3)
abline(h = log(a$sprate), col = "red", lty = 2)

boxplot(Phylogenetic_signal ~ Model, data = data.analysis.comp3, ylim = c(0, 1))
abline(h = a$Phylogenetic_signal, col = "red", lty = 2)


```



